We develop a convolutional neural network (CNN) model to predict the diffusivity of cations in nanoparticle-based electrolytes and use it to identify the characteristics of morphologies that exhibit optimal transport properties. The ground truth data are obtained from kinetic Monte Carlo (kMC) simulations of cation transport parametrized using a multiscale modeling strategy. We implement deep learning approaches to quantitatively link the diffusivity of cations to the spatial arrangement of the nanoparticles. We then integrate the trained CNN model with a topology optimization algorithm for accelerated discovery of nanoparticle morphologies that exhibit optimal cation diffusivities at a specified nanoparticle loading, and we investigate the ability of the CNN model to quantitatively account for the influence of interparticle spatial correlations on cation diffusivity. Finally, by using data-driven approaches, we explore how simple descriptors of nanoparticle morphology correlate with cation diffusivity, thus providing a physical rationale for the observed optimal microstructures. The results of this study highlight the capability of CNNs to serve as surrogate models for structure-property relationships in composites with monodisperse spherical particles, which can in turn be used with inverse methods to discover morphologies that produce optimal target properties.A stable water-in-water (W/W) emulsion was formed by mixing dextran and hydroxypropyl methylcellulose (HPMC) with addition of β-lactoglobulin (Blg) microgels. https://www.selleckchem.com/products/azd3965.html The microstructure and stability of the W/W emulsion were investigated under different conditions. The microgels accumulating at the liquid-liquid interface led to a stable emulsion at pH 3-5, where the microgels carried positive charges. When the pH was increased above the pI of microgels (?pH 5), the emulsion was destabilized because the microgels tended to stay in the continuous phase (i.e., dextran) rather than at the interface. The HPMC-in-dextran emulsions were stable under ionic strength levels up to 300 mM. The HPMC-in-dextran emulsion stabilized by Blg microgels was thermally stable, and the heat treatment promoted partial Blg microgel particle-particle fusion on the surface of HPMC droplets at 90 °C. Electrostatic and hydrophobic interactions between dextran and HPMC phase were further investigated to understand the microgels' accumulation at the liquid-liquid interface.Metal atomic chains have been reported to change their electronic or magnetic properties by slight mechanical stimulus. However, the mechanical response has been veiled because of lack of information on the bond nature. Here, we clarify the bond nature in platinum (Pt) monatomic chains by our in situ transmission electron microscope method. The stiffness is measured with sub-N/m precision by quartz length-extension resonator. The bond stiffnesses at the middle of the chain and at the connection to the base are estimated to be 25 and 23 N/m, respectively, which are higher than the bulk counterpart. Interestingly, the bond length of 0.25 nm is found to be elastically stretched to 0.31 nm, corresponding to a 24% strain. Such peculiar bond nature could be explained by a novel concept of "string tension". This study is a milestone that will significantly change the way we think about atomic bonds in one-dimension.Ionic liquids (ILs) are designer solvents that find wide applications in various areas. Recently, ILs have been shown to induce the refolding of certain proteins that were previously denatured under the treatment of urea. A molecular-level understanding of the counteracting mechanism of ILs on urea-induced protein denaturation remains elusive. In this study, we employ atomistic molecular dynamics simulations to investigate the ternary urea-water-IL solution in comparison to the aqueous urea solution to understand how the presence of ILs can modulate the structure, energetics, and dynamics of urea-water solutions. Our results show that the ions of the IL used, ethylammonium nitrate (EAN), interact strongly with urea and disrupt the urea aggregates that were known to stabilize the unfolded state of the proteins. Results also suggest a disruption in urea-water interaction that releases more free water molecules in solution. We subsequently strengthened these findings by simulating a model peptide in the absence and presence of EAN, which showed broken versus intact secondary structure in urea solution. Analyses show that these changes were accomplished by the added IL, which enforced a gradual displacement of urea from the peptide surface by water. We propose that the ILs facilitate protein renaturation by breaking down the urea aggregates and increasing the amount of free water molecules around the protein.Electrostatic forces drive a wide variety of biomolecular processes by defining the energetics of the interaction between biomolecules and charged substances. Molecular dynamics (MD) simulations provide trajectories that contain ensembles of structural configurations sampled by biomolecules and their environment. Although this information can be used for high-resolution characterization of biomolecular electrostatics, it has not yet been possible to calculate electrostatic potentials from MD trajectories in a way allowing for quantitative connection to energetics. Here, we present g_elpot, a GROMACS-based tool that utilizes the smooth particle mesh Ewald method to quantify the electrostatics of biomolecules by calculating potential within water molecules that are explicitly present in biomolecular MD simulations. g_elpot can extract the global distribution of the electrostatic potential from MD trajectories and measure its time course in functionally important regions of a biomolecule. To demonstrate that g_elpot can be used to gain biophysical insights into various biomolecular processes, we applied the tool to MD trajectories of the P2X3 receptor, TMEM16 lipid scramblases, the secondary-active transporter GltPh, and DNA complexed with cationic polymers. Our results indicate that g_elpot is well suited for quantifying electrostatics in biomolecular systems to provide a deeper understanding of its role in biomolecular processes.